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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LiLT/%5BHuggingFace_Trainer%5D_Fine_tune_LiltForTokenClassification_on_FUNSD_(nielsr_funsd_layoutlmv3).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set-up environment\n",
        "\n",
        "First, we install 🤗 Transformers and Datasets."
      ],
      "metadata": {
        "id": "A8IXvuiRkuoX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cu_Z2ITHkqSE",
        "outputId": "fbbc9779-f0d6-410f-e2ff-1584c96323cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'transformers': No such file or directory\n",
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 141060, done.\u001b[K\n",
            "remote: Counting objects: 100% (29/29), done.\u001b[K\n",
            "remote: Compressing objects: 100% (21/21), done.\u001b[K\n",
            "remote: Total 141060 (delta 7), reused 18 (delta 5), pack-reused 141031\u001b[K\n",
            "Receiving objects: 100% (141060/141060), 117.41 MiB | 14.07 MiB/s, done.\n",
            "Resolving deltas: 100% (104136/104136), done.\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 163 kB 15.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 7.0 MB 67.5 MB/s \n",
            "\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets"
      ],
      "metadata": {
        "id": "dTy0InOUk06d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49f04486-6f0c-48f4-ec05-c1192ab90477"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 431 kB 14.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 34.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 36.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 32.3 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load dataset\n",
        "\n",
        "Let's load the FUNSD dataset from the hub. Note that this dataset contains segment positions, which will help in boosting the performance (as shown in [this paper](https://arxiv.org/abs/2105.11210))."
      ],
      "metadata": {
        "id": "2XPnm5jfk3LJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"nielsr/funsd-layoutlmv3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204,
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        },
        "id": "DIkMH0NMk22s",
        "outputId": "bfd58f92-ae9e-4022-eeae-73f18b09caae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.13k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67cdb097f20f45aa8e652355a461e1cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset funsd-layoutlmv3/funsd to /root/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/16.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a66dbdfe9fa3431dac92f54ea8352df8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cb4687a80c24e64a2f2e598f13810ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd98aa79f6264f4987e017c9d0de865e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset funsd-layoutlmv3 downloaded and prepared to /root/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "202c52a6d8a24b95af238f9f72b9b285"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tP9gkeMall9T",
        "outputId": "dea4d33a-bf8d-430a-aef7-1d487a557bae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'bboxes', 'ner_tags', 'image'],\n",
              "        num_rows: 149\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'bboxes', 'ner_tags', 'image'],\n",
              "        num_rows: 50\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"].features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9C2Kesh-mV25",
        "outputId": "5097a1fb-abef-402a-c3a3-575cd7380091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': Value(dtype='string', id=None),\n",
              " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'bboxes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None),\n",
              " 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER'], id=None), length=-1, id=None),\n",
              " 'image': Image(decode=True, id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll create id2label and label2id mappings here, useful for inference."
      ],
      "metadata": {
        "id": "osf5lHmmcIxR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_list = dataset[\"train\"].features['ner_tags'].feature.names\n",
        "id2label = {id:label for id, label in enumerate(label_list)}\n",
        "label2id = {label:id for id, label in enumerate(label_list)}\n",
        "print(id2label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2H-26gvbmWrv",
        "outputId": "8c3a0169-55e6-4a42-efb1-0cdf4034ed90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'O', 1: 'B-HEADER', 2: 'I-HEADER', 3: 'B-QUESTION', 4: 'I-QUESTION', 5: 'B-ANSWER', 6: 'I-ANSWER'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset[\"train\"][0]\n",
        "print(example[\"tokens\"])\n",
        "print(example[\"bboxes\"])\n",
        "print(example[\"ner_tags\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJh06Im6lzpA",
        "outputId": "344f7c9c-7210-4b2c-d82c-ed9139cd3131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['R&D', ':', 'Suggestion:', 'Date:', 'Licensee', 'Yes', 'No', '597005708', 'R&D', 'QUALITY', 'IMPROVEMENT', 'SUGGESTION/', 'SOLUTION', 'FORM', 'Name', '/', 'Phone', 'Ext.', ':', 'M.', 'Hamann', 'P.', 'Harper,', 'P.', 'Martinez', '9/', '3/', '92', 'R&D', 'Group:', 'J.', 'S.', 'Wigand', 'Supervisor', '/', 'Manager', 'Discontinue', 'coal', 'retention', 'analyses', 'on', 'licensee', 'submitted', 'product', 'samples', '(Note', ':', 'Coal', 'Retention', 'testing', 'is', 'not', 'performed', 'by', 'most', 'licensees.', 'Other', 'B&W', 'physical', 'measurements', 'as', 'ends', 'stability', 'and', 'inspection', 'for', 'soft', 'spots', 'in', 'ciparettes', 'are', 'thought', 'to', 'be', 'sufficient', 'measures', 'to', 'assure', 'cigarette', 'physical', 'integrity.', 'The', 'proposed', 'action', 'will', 'increase', 'laboratory', 'productivity', '.', ')', 'Suggested', 'Solutions', '(s)', ':', 'Delete', 'coal', 'retention', 'from', 'the', 'list', 'of', 'standard', 'analyses', 'performed', 'on', 'licensee', 'submitted', 'product', 'samples.', 'Special', 'requests', 'for', 'coal', 'retention', 'testing', 'could', 'still', 'be', 'submitted', 'on', 'an', 'exception', 'basis.', 'Have', 'you', 'contacted', 'your', 'Manager/', 'Supervisor?', 'Manager', 'Comments:', 'Manager,', 'please', 'contact', 'suggester', 'and', 'forward', 'comments', 'to', 'the', 'Quality', 'Council.', 'qip', '.', 'wp']\n",
            "[[383, 91, 493, 175], [287, 316, 295, 327], [124, 355, 221, 370], [632, 268, 679, 282], [670, 309, 748, 323], [604, 605, 633, 619], [715, 603, 738, 617], [688, 904, 841, 926], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [712, 264, 774, 279], [712, 264, 774, 279], [712, 264, 774, 279], [551, 310, 644, 323], [551, 310, 644, 323], [309, 313, 429, 327], [309, 313, 429, 327], [309, 313, 429, 327], [119, 316, 286, 331], [119, 316, 286, 331], [119, 316, 286, 331], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [124, 486, 328, 504], [124, 486, 328, 504], [124, 486, 328, 504], [124, 486, 328, 504], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [128, 651, 276, 665], [128, 651, 276, 665], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [133, 823, 190, 838], [133, 823, 190, 838], [133, 823, 190, 838]]\n",
            "[0, 3, 3, 3, 5, 3, 3, 0, 1, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 5, 6, 6, 3, 4, 5, 6, 6, 3, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 2, 2, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transform dataset\n",
        "\n",
        "Next, we'll use LayoutLMv3's tokenizer to prepare data for the model. The reason we can use this tokenizer is because it has the same vocabulary as roberta-base (which we'll combine with LiLT)."
      ],
      "metadata": {
        "id": "o-vaY5EvlOR_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/layoutlmv3-base\")"
      ],
      "metadata": {
        "id": "NOfewDoYlN_8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "ad1cff5a1587432f85620097677dd811",
            "c939ca895482494882866580b153bd02",
            "73e21d6344804e2697f7d8c99fd679a6",
            "6af85ba6ae5948f3b2253fd1b1848992",
            "eb464adacf5b490fa4acc84b748890e8",
            "99aef31003644913b50bd05e2cdf32b5",
            "abd8cb009e20450eb5f3375601d82213",
            "84e86c823e7643878caf147cf47e5a0f",
            "fda91fe2345c445ab62e5345c3536ce4",
            "d0cc235748414b079c0c9f428a32127c",
            "247c212acb514f278e21c5c66c07e6d2",
            "895d5afdd64e4140b7224bd04a9292a8",
            "7a9d2dfaddb849a092a5242774253558",
            "66d44b418d494830aa4dc33529050da1",
            "0c70c88b4bda4baba327ea58610db82b",
            "fbf703944bb8400d88e27811112ccbca",
            "3a1a5b8a2073401b9428aec5f5660f09",
            "f98bae2ccc474e6aaa634e80309e226a",
            "57916bc82f2e4b92a5ed3416c578e7d0",
            "7565570cafa04d5891219b27b4eb470e",
            "cb8c3f04307f4e4c8d5d565ba6ac4846",
            "8959e23becd14e89a645fc09975f22db",
            "63d8a68b01a846ad810ae16d2ec79b37",
            "8072637e21284fb9a81749fae07ea863",
            "81f092d20bac4ec6b22276803c7c171f",
            "8307e5fd098a4b11909200596e56513e",
            "771ed2e1c95b4afbbbbf586f1c605da5",
            "f0e084ec3aa44b0ba29ef3a5c37d8923",
            "fa7cbc03b47645b69f8eec0adaab17d2",
            "dbc870f7a3df42aaaac173a4c95d2013",
            "4f8c1014db4b4a0b8822e80c113c5fb5",
            "73a31e28e59a4efa8c4b55800d637ba3",
            "bc303b035a154726b4b3ae4e230635d5"
          ]
        },
        "outputId": "4cf248b3-90cb-4c60-9937-7ce6a32895b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.14k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad1cff5a1587432f85620097677dd811"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "895d5afdd64e4140b7224bd04a9292a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63d8a68b01a846ad810ae16d2ec79b37"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll use [set_transform](https://huggingface.co/docs/datasets/process#format-transform) here to do the preprocessing on-the-fly."
      ],
      "metadata": {
        "id": "qTZSHBJRgWBA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_examples(batch):\n",
        "  encoding = tokenizer(batch[\"tokens\"],\n",
        "                        boxes=batch[\"bboxes\"],\n",
        "                        word_labels=batch[\"ner_tags\"],\n",
        "                        padding=\"max_length\",\n",
        "                        max_length=512,\n",
        "                        truncation=True,\n",
        "                        return_tensors=\"pt\")\n",
        "  \n",
        "  return encoding\n",
        "\n",
        "dataset.set_transform(prepare_examples)"
      ],
      "metadata": {
        "id": "gZafEzldlXHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's verify an example:"
      ],
      "metadata": {
        "id": "Z6sz_KAXoqFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset[\"train\"][0]\n",
        "print(example.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFEULPT9l79W",
        "outputId": "9868a94e-eae5-4b14-94fa-ad52a30b355a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'attention_mask', 'bbox', 'labels'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(example[\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "zh4uwlMdmBNL",
        "outputId": "d894a69f-2115-4f07-d51b-350dafcae51f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<s> R&D : Suggestion: Date: Licensee Yes No 597005708 R&D QUALITY IMPROVEMENT SUGGESTION/ SOLUTION FORM Name / Phone Ext. : M. Hamann P. Harper, P. Martinez 9/ 3/ 92 R&D Group: J. S. Wigand Supervisor / Manager Discontinue coal retention analyses on licensee submitted product samples (Note : Coal Retention testing is not performed by most licensees. Other B&W physical measurements as ends stability and inspection for soft spots in ciparettes are thought to be sufficient measures to assure cigarette physical integrity. The proposed action will increase laboratory productivity. ) Suggested Solutions (s) : Delete coal retention from the list of standard analyses performed on licensee submitted product samples. Special requests for coal retention testing could still be submitted on an exception basis. Have you contacted your Manager/ Supervisor? Manager Comments: Manager, please contact suggester and forward comments to the Quality Council. qip. wp</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for id, box, label in zip(example[\"input_ids\"].tolist(),\n",
        "                          example[\"bbox\"].tolist(),\n",
        "                          example[\"labels\"].tolist()):\n",
        "  if label != -100:\n",
        "    print(tokenizer.decode([id]), box, id2label[label])\n",
        "  else:\n",
        "    print(tokenizer.decode([id]), box, label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHbNy2twmHYr",
        "outputId": "bb8e765d-51f4-4596-ac45-8af4baf66948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [0, 0, 0, 0] -100\n",
            " R [383, 91, 493, 175] O\n",
            "& [383, 91, 493, 175] -100\n",
            "D [383, 91, 493, 175] -100\n",
            " : [287, 316, 295, 327] B-QUESTION\n",
            " Suggest [124, 355, 221, 370] B-QUESTION\n",
            "ion [124, 355, 221, 370] -100\n",
            ": [124, 355, 221, 370] -100\n",
            " Date [632, 268, 679, 282] B-QUESTION\n",
            ": [632, 268, 679, 282] -100\n",
            " License [670, 309, 748, 323] B-ANSWER\n",
            "e [670, 309, 748, 323] -100\n",
            " Yes [604, 605, 633, 619] B-QUESTION\n",
            " No [715, 603, 738, 617] B-QUESTION\n",
            " 5 [688, 904, 841, 926] O\n",
            "97 [688, 904, 841, 926] -100\n",
            "005 [688, 904, 841, 926] -100\n",
            "708 [688, 904, 841, 926] -100\n",
            " R [335, 201, 555, 229] B-HEADER\n",
            "& [335, 201, 555, 229] -100\n",
            "D [335, 201, 555, 229] -100\n",
            " QU [335, 201, 555, 229] I-HEADER\n",
            "AL [335, 201, 555, 229] -100\n",
            "ITY [335, 201, 555, 229] -100\n",
            " IM [335, 201, 555, 229] I-HEADER\n",
            "PROV [335, 201, 555, 229] -100\n",
            "EMENT [335, 201, 555, 229] -100\n",
            " S [335, 201, 555, 229] I-HEADER\n",
            "UG [335, 201, 555, 229] -100\n",
            "G [335, 201, 555, 229] -100\n",
            "EST [335, 201, 555, 229] -100\n",
            "ION [335, 201, 555, 229] -100\n",
            "/ [335, 201, 555, 229] -100\n",
            " SOL [335, 201, 555, 229] I-HEADER\n",
            "UTION [335, 201, 555, 229] -100\n",
            " FORM [335, 201, 555, 229] I-HEADER\n",
            " Name [116, 272, 267, 289] B-QUESTION\n",
            " / [116, 272, 267, 289] I-QUESTION\n",
            " Phone [116, 272, 267, 289] I-QUESTION\n",
            " Ext [116, 272, 267, 289] I-QUESTION\n",
            ". [116, 272, 267, 289] -100\n",
            " : [116, 272, 267, 289] I-QUESTION\n",
            " M [282, 271, 591, 287] B-ANSWER\n",
            ". [282, 271, 591, 287] -100\n",
            " Ham [282, 271, 591, 287] I-ANSWER\n",
            "ann [282, 271, 591, 287] -100\n",
            " P [282, 271, 591, 287] I-ANSWER\n",
            ". [282, 271, 591, 287] -100\n",
            " Harper [282, 271, 591, 287] I-ANSWER\n",
            ", [282, 271, 591, 287] -100\n",
            " P [282, 271, 591, 287] I-ANSWER\n",
            ". [282, 271, 591, 287] -100\n",
            " Martinez [282, 271, 591, 287] I-ANSWER\n",
            " 9 [712, 264, 774, 279] B-ANSWER\n",
            "/ [712, 264, 774, 279] -100\n",
            " 3 [712, 264, 774, 279] I-ANSWER\n",
            "/ [712, 264, 774, 279] -100\n",
            " 92 [712, 264, 774, 279] I-ANSWER\n",
            " R [551, 310, 644, 323] B-QUESTION\n",
            "& [551, 310, 644, 323] -100\n",
            "D [551, 310, 644, 323] -100\n",
            " Group [551, 310, 644, 323] I-QUESTION\n",
            ": [551, 310, 644, 323] -100\n",
            " J [309, 313, 429, 327] B-ANSWER\n",
            ". [309, 313, 429, 327] -100\n",
            " S [309, 313, 429, 327] I-ANSWER\n",
            ". [309, 313, 429, 327] -100\n",
            " W [309, 313, 429, 327] I-ANSWER\n",
            "ig [309, 313, 429, 327] -100\n",
            "and [309, 313, 429, 327] -100\n",
            " Supervisor [119, 316, 286, 331] B-QUESTION\n",
            " / [119, 316, 286, 331] I-QUESTION\n",
            " Manager [119, 316, 286, 331] I-QUESTION\n",
            " Disc [249, 346, 779, 447] B-ANSWER\n",
            "ont [249, 346, 779, 447] -100\n",
            "in [249, 346, 779, 447] -100\n",
            "ue [249, 346, 779, 447] -100\n",
            " coal [249, 346, 779, 447] I-ANSWER\n",
            " retention [249, 346, 779, 447] I-ANSWER\n",
            " analyses [249, 346, 779, 447] I-ANSWER\n",
            " on [249, 346, 779, 447] I-ANSWER\n",
            " licensee [249, 346, 779, 447] I-ANSWER\n",
            " submitted [249, 346, 779, 447] I-ANSWER\n",
            " product [249, 346, 779, 447] I-ANSWER\n",
            " samples [249, 346, 779, 447] I-ANSWER\n",
            " ( [249, 346, 779, 447] I-ANSWER\n",
            "Note [249, 346, 779, 447] -100\n",
            " : [249, 346, 779, 447] I-ANSWER\n",
            " Coal [249, 346, 779, 447] I-ANSWER\n",
            " Ret [249, 346, 779, 447] I-ANSWER\n",
            "ention [249, 346, 779, 447] -100\n",
            " testing [249, 346, 779, 447] I-ANSWER\n",
            " is [249, 346, 779, 447] I-ANSWER\n",
            " not [249, 346, 779, 447] I-ANSWER\n",
            " performed [249, 346, 779, 447] I-ANSWER\n",
            " by [249, 346, 779, 447] I-ANSWER\n",
            " most [249, 346, 779, 447] I-ANSWER\n",
            " license [249, 346, 779, 447] I-ANSWER\n",
            "es [249, 346, 779, 447] -100\n",
            ". [249, 346, 779, 447] -100\n",
            " Other [249, 346, 779, 447] I-ANSWER\n",
            " B [249, 346, 779, 447] I-ANSWER\n",
            "& [249, 346, 779, 447] -100\n",
            "W [249, 346, 779, 447] -100\n",
            " physical [249, 346, 779, 447] I-ANSWER\n",
            " measurements [249, 346, 779, 447] I-ANSWER\n",
            " as [249, 346, 779, 447] I-ANSWER\n",
            " ends [249, 346, 779, 447] I-ANSWER\n",
            " stability [249, 346, 779, 447] I-ANSWER\n",
            " and [249, 346, 779, 447] I-ANSWER\n",
            " inspection [249, 346, 779, 447] I-ANSWER\n",
            " for [249, 346, 779, 447] I-ANSWER\n",
            " soft [249, 346, 779, 447] I-ANSWER\n",
            " spots [249, 346, 779, 447] I-ANSWER\n",
            " in [249, 346, 779, 447] I-ANSWER\n",
            " c [249, 346, 779, 447] I-ANSWER\n",
            "ip [249, 346, 779, 447] -100\n",
            "arettes [249, 346, 779, 447] -100\n",
            " are [249, 346, 779, 447] I-ANSWER\n",
            " thought [249, 346, 779, 447] I-ANSWER\n",
            " to [249, 346, 779, 447] I-ANSWER\n",
            " be [249, 346, 779, 447] I-ANSWER\n",
            " sufficient [249, 346, 779, 447] I-ANSWER\n",
            " measures [249, 346, 779, 447] I-ANSWER\n",
            " to [249, 346, 779, 447] I-ANSWER\n",
            " assure [249, 346, 779, 447] I-ANSWER\n",
            " cigarette [249, 346, 779, 447] I-ANSWER\n",
            " physical [249, 346, 779, 447] I-ANSWER\n",
            " integrity [249, 346, 779, 447] I-ANSWER\n",
            ". [249, 346, 779, 447] -100\n",
            " The [249, 346, 779, 447] I-ANSWER\n",
            " proposed [249, 346, 779, 447] I-ANSWER\n",
            " action [249, 346, 779, 447] I-ANSWER\n",
            " will [249, 346, 779, 447] I-ANSWER\n",
            " increase [249, 346, 779, 447] I-ANSWER\n",
            " laboratory [249, 346, 779, 447] I-ANSWER\n",
            " productivity [249, 346, 779, 447] I-ANSWER\n",
            ". [249, 346, 779, 447] I-ANSWER\n",
            " ) [249, 346, 779, 447] I-ANSWER\n",
            " Suggest [124, 486, 328, 504] B-QUESTION\n",
            "ed [124, 486, 328, 504] -100\n",
            " Solutions [124, 486, 328, 504] I-QUESTION\n",
            " ( [124, 486, 328, 504] I-QUESTION\n",
            "s [124, 486, 328, 504] -100\n",
            ") [124, 486, 328, 504] -100\n",
            " : [124, 486, 328, 504] I-QUESTION\n",
            " Delete [345, 483, 778, 553] B-ANSWER\n",
            " coal [345, 483, 778, 553] I-ANSWER\n",
            " retention [345, 483, 778, 553] I-ANSWER\n",
            " from [345, 483, 778, 553] I-ANSWER\n",
            " the [345, 483, 778, 553] I-ANSWER\n",
            " list [345, 483, 778, 553] I-ANSWER\n",
            " of [345, 483, 778, 553] I-ANSWER\n",
            " standard [345, 483, 778, 553] I-ANSWER\n",
            " analyses [345, 483, 778, 553] I-ANSWER\n",
            " performed [345, 483, 778, 553] I-ANSWER\n",
            " on [345, 483, 778, 553] I-ANSWER\n",
            " licensee [345, 483, 778, 553] I-ANSWER\n",
            " submitted [345, 483, 778, 553] I-ANSWER\n",
            " product [345, 483, 778, 553] I-ANSWER\n",
            " samples [345, 483, 778, 553] I-ANSWER\n",
            ". [345, 483, 778, 553] -100\n",
            " Special [345, 483, 778, 553] I-ANSWER\n",
            " requests [345, 483, 778, 553] I-ANSWER\n",
            " for [345, 483, 778, 553] I-ANSWER\n",
            " coal [345, 483, 778, 553] I-ANSWER\n",
            " retention [345, 483, 778, 553] I-ANSWER\n",
            " testing [345, 483, 778, 553] I-ANSWER\n",
            " could [345, 483, 778, 553] I-ANSWER\n",
            " still [345, 483, 778, 553] I-ANSWER\n",
            " be [345, 483, 778, 553] I-ANSWER\n",
            " submitted [345, 483, 778, 553] I-ANSWER\n",
            " on [345, 483, 778, 553] I-ANSWER\n",
            " an [345, 483, 778, 553] I-ANSWER\n",
            " exception [345, 483, 778, 553] I-ANSWER\n",
            " basis [345, 483, 778, 553] I-ANSWER\n",
            ". [345, 483, 778, 553] -100\n",
            " Have [125, 608, 522, 624] B-HEADER\n",
            " you [125, 608, 522, 624] I-HEADER\n",
            " contacted [125, 608, 522, 624] I-HEADER\n",
            " your [125, 608, 522, 624] I-HEADER\n",
            " Manager [125, 608, 522, 624] I-HEADER\n",
            "/ [125, 608, 522, 624] -100\n",
            " Supervisor [125, 608, 522, 624] I-HEADER\n",
            "? [125, 608, 522, 624] -100\n",
            " Manager [128, 651, 276, 665] B-QUESTION\n",
            " Comments [128, 651, 276, 665] I-QUESTION\n",
            ": [128, 651, 276, 665] -100\n",
            " Manager [304, 644, 717, 662] B-ANSWER\n",
            ", [304, 644, 717, 662] -100\n",
            " please [304, 644, 717, 662] I-ANSWER\n",
            " contact [304, 644, 717, 662] I-ANSWER\n",
            " sugg [304, 644, 717, 662] I-ANSWER\n",
            "ester [304, 644, 717, 662] -100\n",
            " and [304, 644, 717, 662] I-ANSWER\n",
            " forward [304, 644, 717, 662] I-ANSWER\n",
            " comments [129, 662, 423, 677] B-ANSWER\n",
            " to [129, 662, 423, 677] I-ANSWER\n",
            " the [129, 662, 423, 677] I-ANSWER\n",
            " Quality [129, 662, 423, 677] I-ANSWER\n",
            " Council [129, 662, 423, 677] I-ANSWER\n",
            ". [129, 662, 423, 677] -100\n",
            " q [133, 823, 190, 838] O\n",
            "ip [133, 823, 190, 838] -100\n",
            ". [133, 823, 190, 838] O\n",
            " w [133, 823, 190, 838] O\n",
            "p [133, 823, 190, 838] -100\n",
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            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n",
            "<pad> [0, 0, 0, 0] -100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load model\n",
        "\n",
        "Here we load the LiLT, combined with RoBERTa-base, from the hub."
      ],
      "metadata": {
        "id": "MKXF-3lsnPf8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"SCUT-DLVCLab/lilt-roberta-en-base\", id2label=id2label, label2id=label2id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "b7cd4a2b13094d1faf0649ba0219afe8",
            "c90f08bc6dad450993ea7b007b74c366",
            "367c48d81d1b43489139b970d5918797",
            "8694fbe8b0c7495cbab06af776d3dddc",
            "c4f2579ddcbe43dab5b8a39ab13f2131",
            "c3d6b2a5a6d44b1e94e3b464bc8cf07b",
            "a6c7f5900df14c88bd4dfb9f0ca49d8c",
            "bd9630abcc13422e9fc45b2ddaa8fae4",
            "3882728b316e44ad8c4de37199bf9d8a",
            "31458524e3cb4ea6abb439b5c8f27c54",
            "7b4e0879d98f45128b16df01c73ad994",
            "a3614084c82643589718bacf408fc8ec",
            "5151091a9f284a5fa20b1cf0d17612d7",
            "82e29d9d7635471a9b6fd9eb161e6a8a",
            "d300bdad59ac41b692c1d6c653870d75",
            "57293f16f2b041ea866839fda9690033",
            "ce3922d71582483b927ba0471bf3f578",
            "38cb74b8eea74140a42ff9fe7f7118a4",
            "03a60bf7ebd1411a8d3a31abe954f76a",
            "17bed38c39aa48a7905729cc70224424",
            "0c09c880b1cd48929a461b14b9b06296",
            "415fa2942d0b42ea946b311d1e8ab544"
          ]
        },
        "id": "iCEu5e6snDoZ",
        "outputId": "2c0e91e7-9b8a-48f9-c0f6-526849951a17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/789 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7cd4a2b13094d1faf0649ba0219afe8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/523M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3614084c82643589718bacf408fc8ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of LiltForTokenClassification were not initialized from the model checkpoint at nielsr/lilt-roberta-en-base and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train!\n",
        "\n",
        "Next, we'll train the model using 🤗 Trainer. Check the docs [here](https://huggingface.co/docs/transformers/main_classes/trainer) for all details.\n",
        "\n",
        "We define a compute_metrics function which will be used to compute metrics like F1 score, precision and recall (per entity type) on the evaluation set during training. We use 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) for that."
      ],
      "metadata": {
        "id": "oLcw2HVFnW24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q evaluate seqeval"
      ],
      "metadata": {
        "id": "dP-lOqbDqXzo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69cf9897-552e-44dc-8d99-0f9f954409e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 69 kB 813 kB/s \n",
            "\u001b[K     |████████████████████████████████| 43 kB 2.2 MB/s \n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Metric\n",
        "metric = evaluate.load(\"seqeval\")\n",
        "\n",
        "return_entity_level_metrics = False\n",
        "\n",
        "# Taken from the token-classification example\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    if return_entity_level_metrics:\n",
        "        # Unpack nested dictionaries\n",
        "        final_results = {}\n",
        "        for key, value in results.items():\n",
        "            if isinstance(value, dict):\n",
        "                for n, v in value.items():\n",
        "                    final_results[f\"{key}_{n}\"] = v\n",
        "            else:\n",
        "                final_results[key] = value\n",
        "        return final_results\n",
        "    else:\n",
        "        return {\n",
        "            \"precision\": results[\"overall_precision\"],\n",
        "            \"recall\": results[\"overall_recall\"],\n",
        "            \"f1\": results[\"overall_f1\"],\n",
        "            \"accuracy\": results[\"overall_accuracy\"],\n",
        "        }"
      ],
      "metadata": {
        "id": "hBzkTkYrp8x3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0d3c950e6c61442b89b00c4424262e99",
            "729ce9a2392041ccb725ccd48a706e83",
            "fb712aed972b434eb1871755f838a627",
            "db0838ae229c47868f40ac1ccf656edd",
            "75ee7b07526d4f589c85a845d85651c8",
            "237b7132f94d4ffa8017d2c50b170448",
            "710cd2105c604e3693309e999c5294a8",
            "ac48679c5bf3464e90ad9f3a01c2ca52",
            "d161d86add304bfb8ff9b6b4b481dd22",
            "6f32d3cda87140739fc2c0b6e5bb899e",
            "de1a01a0804c4013b914a7fb12013287"
          ]
        },
        "outputId": "5a3ab20f-5895-4579-897c-e1acc84ccc83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d3c950e6c61442b89b00c4424262e99"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also log in to our HuggingFace account as we'll push the model to the hub during training."
      ],
      "metadata": {
        "id": "IQhmEdMZszX7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BI9lOFdvD6vb",
        "outputId": "19457f38-0b05-4002-bc6b-d7aec99d437d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "args = TrainingArguments(output_dir=\"lilt-roberta-en-base-finetuned-funsd\",\n",
        "                         overwrite_output_dir=True,\n",
        "                         remove_unused_columns=False,\n",
        "                         warmup_steps=0.1,\n",
        "                         max_steps=2000,\n",
        "                         evaluation_strategy=\"steps\",\n",
        "                         eval_steps=100,\n",
        "                         push_to_hub=True)\n",
        "\n",
        "trainer = Trainer(\n",
        "        model=model,\n",
        "        args=args,\n",
        "        train_dataset=dataset[\"train\"],\n",
        "        eval_dataset=dataset[\"test\"],\n",
        "        tokenizer=tokenizer,\n",
        "        compute_metrics=compute_metrics,\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZjVCAFcnWDO",
        "outputId": "3948595d-c081-4743-b3b5-96703192118b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "/content/lilt-roberta-en-base-finetuned-funsd is already a clone of https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/lilt-roberta-en-base-finetuned-funsd is already a clone of https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's train!"
      ],
      "metadata": {
        "id": "xZfR3Ht5s3hI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T2Seud0v7B0_",
        "outputId": "f942a590-6832-4619-da2d-a865e2c7425c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 149\n",
            "  Num Epochs = 106\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2000' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2000/2000 28:07, Epoch 105/106]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.178920</td>\n",
              "      <td>0.850598</td>\n",
              "      <td>0.848485</td>\n",
              "      <td>0.849540</td>\n",
              "      <td>0.786877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.238219</td>\n",
              "      <td>0.836011</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>0.856866</td>\n",
              "      <td>0.796981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.376612</td>\n",
              "      <td>0.855710</td>\n",
              "      <td>0.889717</td>\n",
              "      <td>0.872382</td>\n",
              "      <td>0.790919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.559013</td>\n",
              "      <td>0.836812</td>\n",
              "      <td>0.876304</td>\n",
              "      <td>0.856103</td>\n",
              "      <td>0.779151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.437926</td>\n",
              "      <td>0.856178</td>\n",
              "      <td>0.881272</td>\n",
              "      <td>0.868543</td>\n",
              "      <td>0.799239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.539659</td>\n",
              "      <td>0.859256</td>\n",
              "      <td>0.894685</td>\n",
              "      <td>0.876612</td>\n",
              "      <td>0.805420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.613237</td>\n",
              "      <td>0.862052</td>\n",
              "      <td>0.872330</td>\n",
              "      <td>0.867160</td>\n",
              "      <td>0.793296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.648337</td>\n",
              "      <td>0.856595</td>\n",
              "      <td>0.887233</td>\n",
              "      <td>0.871645</td>\n",
              "      <td>0.777725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.659324</td>\n",
              "      <td>0.864101</td>\n",
              "      <td>0.881272</td>\n",
              "      <td>0.872602</td>\n",
              "      <td>0.789492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>1.670427</td>\n",
              "      <td>0.859452</td>\n",
              "      <td>0.871833</td>\n",
              "      <td>0.865598</td>\n",
              "      <td>0.792464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>1.679468</td>\n",
              "      <td>0.849473</td>\n",
              "      <td>0.880278</td>\n",
              "      <td>0.864601</td>\n",
              "      <td>0.774753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>1.551511</td>\n",
              "      <td>0.860432</td>\n",
              "      <td>0.891207</td>\n",
              "      <td>0.875549</td>\n",
              "      <td>0.799120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>1.666499</td>\n",
              "      <td>0.857349</td>\n",
              "      <td>0.886736</td>\n",
              "      <td>0.871795</td>\n",
              "      <td>0.782123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>1.589318</td>\n",
              "      <td>0.860376</td>\n",
              "      <td>0.887730</td>\n",
              "      <td>0.873839</td>\n",
              "      <td>0.789492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.561282</td>\n",
              "      <td>0.860308</td>\n",
              "      <td>0.887233</td>\n",
              "      <td>0.873563</td>\n",
              "      <td>0.812314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.585299</td>\n",
              "      <td>0.852099</td>\n",
              "      <td>0.887233</td>\n",
              "      <td>0.869311</td>\n",
              "      <td>0.803994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.653889</td>\n",
              "      <td>0.870715</td>\n",
              "      <td>0.883259</td>\n",
              "      <td>0.876942</td>\n",
              "      <td>0.807679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.663361</td>\n",
              "      <td>0.878653</td>\n",
              "      <td>0.881272</td>\n",
              "      <td>0.879960</td>\n",
              "      <td>0.807916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.653369</td>\n",
              "      <td>0.880988</td>\n",
              "      <td>0.886239</td>\n",
              "      <td>0.883606</td>\n",
              "      <td>0.807322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.000400</td>\n",
              "      <td>1.655215</td>\n",
              "      <td>0.876167</td>\n",
              "      <td>0.885743</td>\n",
              "      <td>0.880929</td>\n",
              "      <td>0.806847</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to lilt-roberta-en-base-finetuned-funsd/checkpoint-500\n",
            "Configuration saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-500/config.json\n",
            "Model weights saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-500/special_tokens_map.json\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to lilt-roberta-en-base-finetuned-funsd/checkpoint-1000\n",
            "Configuration saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1000/config.json\n",
            "Model weights saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to lilt-roberta-en-base-finetuned-funsd/checkpoint-1500\n",
            "Configuration saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1500/config.json\n",
            "Model weights saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-1500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to lilt-roberta-en-base-finetuned-funsd/checkpoint-2000\n",
            "Configuration saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-2000/config.json\n",
            "Model weights saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/checkpoint-2000/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2000, training_loss=0.011410338178277015, metrics={'train_runtime': 1688.2765, 'train_samples_per_second': 9.477, 'train_steps_per_second': 1.185, 'total_flos': 4362983351362560.0, 'train_loss': 0.011410338178277015, 'epoch': 105.26})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate\n",
        "\n",
        "Evaluation can be done as follows:"
      ],
      "metadata": {
        "id": "D3ADz6LMsU43"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = trainer.evaluate()\n",
        "print(metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "6mO1t_W6oXsU",
        "outputId": "c230bf79-3d66-4a37-dddc-1a607853bc58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7/7 00:02]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 1.655214786529541, 'eval_precision': 0.8761670761670761, 'eval_recall': 0.8857426726279185, 'eval_f1': 0.8809288537549407, 'eval_accuracy': 0.8068465470105789, 'eval_runtime': 2.9687, 'eval_samples_per_second': 16.842, 'eval_steps_per_second': 2.358, 'epoch': 105.26}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics['eval_f1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeczWHO0AGl7",
        "outputId": "fc98c8e2-4c67-4a68-b547-b436e5856118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8809288537549407"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Push to hub\n",
        "\n",
        "Let's push everything to the hub, such that we can reuse the model afterwards using `from_pretrained`. This will also include a model card and TensorBoard metrics.\n",
        "\n",
        "Check the resulting model [here](https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd)."
      ],
      "metadata": {
        "id": "q3HmZ4sQNhPi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578,
          "referenced_widgets": [
            "0fc897923f1441d995f16ea80efdbdef",
            "99f170e315134e33b82f26ac02be0556",
            "faf47108f83841b68915948ee28bd716",
            "652bf864df3b4d979d6fcf800d894f77",
            "b8eea0cb5d2347c1b8e45e85f6b7d880",
            "d374db83de774a2a8c4e7e4050ec80b9",
            "e9860b133df14904981898851cceafa2",
            "3ac9fb04bb9342dd8d25f73a4932a729",
            "3446d3d63438447f9144f98eaa4101e7",
            "f8f0dde8e0324757bdf26906a88136a4",
            "bb4d91449c664b6481fc6702e91b12f1",
            "38535f5382c44494a5b0584b6bd0f3ff",
            "53611fb69c794b22a9b1561f01f5f682",
            "5aa7981530b842ec98888c1b74588993",
            "6cd46572bc4d4f689240de4936f6733d",
            "354edd6e4892461e8048eaa4889c25fc",
            "4821ff13b3354507a7671907c072a6d9",
            "12fbb4dec6f54526bb66cdde58d01df3",
            "cdaf1d7529e148a4a88ca81893c6797a",
            "eaf5b9c9179340f2a526d4f9e338db91",
            "ff76337ae64243d29717c4a89238bd8c",
            "a6e975c6b5b14f5e997110bfbb9bfd2c",
            "79c29a084f83405b9275288c88c0e8e6",
            "e7cd257841174b2e99728824b540159a",
            "b4ea20fa9e684428ae099ef70a0379e6",
            "738d5b0723f54e2a819283aaeac4f97c",
            "cd755a2c38cb4cf8ba3c78e5d53f2121",
            "80e4f05f7ceb4936a530454679857513",
            "c4f7380a5ac1448bafb461ec3f9fbeee",
            "8793e82d21ee41afb7de4a678a16c780",
            "384326aaa810419a939066189287bf31",
            "cc7c291ad1314533949bb4a464f27640",
            "a946f034677c409880782b211c1864ab"
          ]
        },
        "id": "vsUP6SNFNOWF",
        "outputId": "af8c19f1-c13b-4fe2-fb93-0f981d1e4e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to lilt-roberta-en-base-finetuned-funsd\n",
            "Configuration saved in lilt-roberta-en-base-finetuned-funsd/config.json\n",
            "Model weights saved in lilt-roberta-en-base-finetuned-funsd/pytorch_model.bin\n",
            "tokenizer config file saved in lilt-roberta-en-base-finetuned-funsd/tokenizer_config.json\n",
            "Special tokens file saved in lilt-roberta-en-base-finetuned-funsd/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.34k/497M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0fc897923f1441d995f16ea80efdbdef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Sep30_06-31-33_2c83b8a52643/events.out.tfevents.1664521390.2c83b8a52643.68.5: 100%|##########…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38535f5382c44494a5b0584b6bd0f3ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Sep30_06-31-33_2c83b8a52643/events.out.tfevents.1664519498.2c83b8a52643.68.3:  23%|##3       …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79c29a084f83405b9275288c88c0e8e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd\n",
            "   767eda6..661834b  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd\n",
            "   767eda6..661834b  main -> main\n",
            "\n",
            "To https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd\n",
            "   661834b..43a62d7  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd\n",
            "   661834b..43a62d7  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/nielsr/lilt-roberta-en-base-finetuned-funsd/commit/661834b5eeac1997307882ab0e41beccbbd6d414'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xNPFQDfPNgi0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}